Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

Anirudh S; Nidhin Prabhakar T. V; Spoorthi M; Utkarsh Tiwari

REVIEW 3 major objections 4 minor 20 references

Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities

T0 review · 3 major / 4 minor · reviewed 2026-07-11 · grok-4.5

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Pith's one-line read Topic modeling with large language models groups software vulnerability threat descriptions into interpretable clusters that support prioritization.

desk verdict Routine multi-pipeline topic-model demo on Cisco Threat text that never measures whether the clusters actually help prioritization. read the letter →

arxiv 2607.03887 v1 pith:LDEFDFUD submitted 2026-07-04 cs.CR cs.AIcs.SE

Utkarsh Tiwari , Spoorthi M , Anirudh S , Nidhin Prabhakar T. V This is my paper

classification cs.CRcs.AIcs.SE

keywords SoftwareVulnerabilitiesTopicModelingLargeLanguageModelsCybersecurityBERTopicTop2VecDimensionalityReductionClustering

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Software vulnerabilities produce large volumes of unstructured text that traditional methods struggle to organize. This paper shows that several modern topic-modeling pipelines—BERTopic in multiple configurations, Top2Vec, CombinedTM, Llama2-augmented BERTopic, and Mixtral—can be applied to the Threat column of a real vulnerability dataset to surface latent themes and produce readable topic clusters. The resulting groupings reveal recurring patterns such as operating-system issues, encryption weaknesses, and virtualization flaws. A sympathetic reader cares because the approach promises a scalable, automated way to turn raw threat text into structured categories that security teams can use for triage and resource allocation. The work therefore aims to make vulnerability management less manual and more coherent.

What carries the argument

The family of topic-modeling pipelines (BERTopic with UMAP/PCA/HDBSCAN/DBSCAN variants, Top2Vec, CombinedTM aligning bag-of-words with contextual embeddings, Llama2 labeling of BERTopic clusters, and Mixtral document-level prompting) applied directly to the cleaned Threat feature.

What would settle it

Run a blinded human evaluation or a controlled triage task in which security analysts using the generated clusters versus raw text are measured for ranking accuracy, time-to-decision, and inter-rater agreement; if the clusters produce no measurable gain, the central claim fails.

Watch

Extended reading notes

Core claim

When advanced topic-modeling techniques powered by large language models and standard dimensionality-reduction and clustering steps are run on the Threat descriptions of a large software-vulnerability corpus, they uncover latent patterns and generate interpretable clusters that improve threat prioritization and decision-making in cybersecurity.

Load-bearing premise

The clusters and keyword lists produced by the chosen models and hyperparameters are coherent and actionable enough to improve real-world vulnerability prioritization, even without reported numerical coherence scores or human triage experiments.

Editorial extensions

If this is right

Security teams can automatically surface dominant vulnerability themes such as Linux kernel issues or SSL-certificate weaknesses from large text dumps.
Resource allocation can be guided by cluster size and topic prevalence rather than manual scanning of individual reports.
The same pipelines can be re-run on new vulnerability feeds to keep topic maps current without hand-crafted taxonomies.
Outlier clusters produced by the models flag noisy or low-coherence entries that may need separate review.
Scalable automated categorization becomes feasible for organizations facing tens of thousands of threat descriptions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the clusters prove stable across successive dataset snapshots, the same pipeline could feed continuous-integration security dashboards that alert on emerging topic shifts.
The absence of reported coherence metrics suggests a natural next experiment: compute NPMI or C_V scores and correlate them with analyst preference rankings.
Document-level Mixtral topics could be aggregated into a hierarchical map that links individual CVEs to broader campaign-level themes.
Extending the approach to multilingual threat reports would test whether the embedding-plus-clustering core generalizes beyond English Cisco data.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The punchline is simple: this is a clean engineering bake-off of BERTopic (four configs), Top2Vec, CombinedTM, Llama2+BERTopic and Mixtral on the free-text Threat field of a ~70k Cisco vulnerability dump. Nothing algorithmic is new; the authors just run the usual UMAP/PCA + HDBSCAN/DBSCAN stack and show the expected vendor-centric clusters (Red Hat, VMware, SSL, CentOS). That is useful as a practical cookbook for anyone who has never tried modern topic models on vuln advisories.

What they do well is the systematic side-by-side: hyper-parameter tables, word-score bars, heatmaps, dendrograms and a short Llama2 labeling example. The literature section correctly points at the recent LLM-topic papers (GPTopic, ChatGPT explanations, etc.) and the older vuln-analysis work. Pre-processing choices are transparent and the visualizations are readable.

The soft spot is exactly where the stress-test says: the Abstract and Conclusion claim the clusters “enhance threat prioritization and decision-making,” yet Section IV contains zero coherence numbers, silhouette scores, human ratings or any triage experiment. We are left staring at pretty pictures and trusting that the chosen min_cluster_size values produced something actionable. That leap is unsupported. The bibliography also goes off the rails after reference 13—air-pollution IoT papers, hand-gesture OpenCV work and building-collapse detection appear for no reason—which undercuts confidence that the dataset citation itself is solid.

Math is absent (none needed), data are real but not released, and the free parameters are the usual UMAP/HDBSCAN knobs. For a practitioner who just wants a starting recipe this is fine; for anyone looking for evidence that topic models improve real triage it is not. I would not bring it to reading group or cite it. A serious editor can desk-reject on the missing evaluation and the citation mess; it does not need referee time in its present form.

Referee Report

3 major / 4 minor

Summary. The paper applies several modern topic-modeling pipelines (BERTopic under four dimensionality-reduction/clustering configurations, CombinedTM, Top2Vec, Llama2+BERTopic, and Mixtral 8x7b) to the free-text “Threat” column of a Cisco vulnerability dataset (≈70 k rows). After light preprocessing, each model produces clusters or document-level topic labels; the authors present qualitative visualizations (word-score bars, UMAP projections, similarity heatmaps, hierarchical dendrograms) and a three-row topic table for the Llama2 variant. The central claim is that these latent patterns and interpretable clusters enhance threat prioritization and decision-making in cybersecurity, thereby supporting scalable automated vulnerability management.

Significance. If the claim were substantiated, the work would offer a practical, largely automated route from unstructured vulnerability advisories to actionable thematic groupings, which is of clear interest to security operations and vulnerability-management teams. The breadth of models examined (classical neural topic models plus two LLM-based labeling schemes) and the use of a real industrial corpus are strengths. However, the manuscript currently supplies only visual inspection of clusters; without quantitative coherence, clustering-quality, or downstream triage metrics, the claimed operational benefit remains unproven and the incremental contribution over existing BERTopic/Top2Vec applications is modest.

major comments (3)

Abstract, §IV and Conclusion assert that the generated clusters “enhance threat prioritization and decision-making.” Section IV contains only qualitative artifacts (Figs. 3–10, Table V) and never reports topic-coherence scores (NPMI, C_V, …), silhouette/DBI/ARI numbers, inter-annotator agreement, or any A/B or ranking experiment that measures prioritization accuracy or analyst time. Without such evidence the leap from “we obtained clusters” to “these clusters improve prioritization” is unsupported; the central claim therefore rests solely on visual inspection of the chosen hyper-parameter settings.
§III-B and the opening of §IV state that models are compared via “topic coherence and clustering quality,” yet no numerical values appear for any model. Consequently the promised comparative analysis cannot be performed by a reader, and it is impossible to decide which configuration (if any) is preferable for the stated use-case.
The free parameters listed in Tables I–IV (UMAP n_neighbors/n_components, HDBSCAN min_cluster_size, DBSCAN eps, CombinedTM number of topics/epochs, etc.) are set without ablation or sensitivity analysis. Because the only evaluation is qualitative, it is unclear whether the reported clusters are robust or merely artifacts of the particular hyper-parameter choices.

minor comments (4)

References [14]–[20] appear to be unrelated prior work of the authors (IoT air-pollution, face recognition, hand-gesture HCI, building-collapse detection, C-code clustering, quadratic-equation assessment, phishing detection) and do not support the claims made in the text; they should be replaced by the actual dataset citation and relevant topic-modeling literature.
Figure captions for Figs. 8 and 9 are identical (“Heatmap: Visualizing thematic overlaps…”) yet the figures themselves are described as a document-to-topic heatmap and a topic-distribution plot; captions need correction.
Inconsistent naming of the Mixtral model (“Mixtral”, “mixtral 8x7b”, “Mistral AI LLM”) throughout the manuscript should be standardized.
The dataset size is given as 69 909 entries, yet after “unique entries only” filtering the effective N used by each model is never stated; this information is needed for reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper applies external topic-modeling pipelines and reports qualitative clusters without claiming any fitted quantity as an independent prediction.

full rationale

The manuscript is an applied empirical study. It extracts the Threat column from a Cisco vulnerability dataset, runs standard off-the-shelf pipelines (BERTopic under several UMAP/PCA/HDBSCAN/DBSCAN configurations, CombinedTM, Top2Vec, Llama2-labeled BERTopic, Mixtral prompt-based labeling), and presents the resulting word lists, heatmaps, dendrograms and UMAP projections. No equation defines a quantity in terms of itself; no parameter is fitted to a subset of the data and then re-presented as a prediction of a closely related quantity; no uniqueness theorem or ansatz is imported from the authors’ own prior work. The only self-references are ordinary citations of the authors’ unrelated earlier papers on IoT and face recognition, none of which underwrite the topic-modeling claims. Consequently the derivation chain contains no circular step. (Whether the qualitative clusters actually improve prioritization is a separate correctness/evaluation question, not a circularity question.)

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The work rests almost entirely on off-the-shelf libraries and standard embedding assumptions; the only free parameters are the usual clustering hyper-parameters chosen by hand. No new mathematical objects or physical entities are postulated.

free parameters (4)

UMAP n_neighbors / n_components
Hand-chosen (15 or 30 neighbors, 5 components) and never ablated; directly control the geometry fed to the clusterer.
HDBSCAN min_cluster_size
Set to 10 or 150 without justification or sensitivity study; determines which documents become outliers versus topics.
DBSCAN eps / min_samples
eps=0.5, min_samples=5 chosen ad hoc; controls density threshold for the third BERTopic configuration.
CombinedTM number of topics / epochs
Fixed at 20 topics and 10 epochs; no search reported.

assumptions (3)

domain assumption Pre-trained sentence embeddings (all-mpnet-base-v2, BAAI/bge-small-en, RoBERTa+GloVe) preserve the semantic distinctions relevant to vulnerability triage.
Invoked throughout Section III without domain-specific fine-tuning or validation against security labels.
ad hoc to paper Topic coherence and cluster separability visible in heatmaps and dendrograms are sufficient proxies for usefulness in prioritization.
Stated as the evaluation goal in Section III.B yet never quantified or linked to any security outcome metric.
standard math Standard linear-algebra and clustering mathematics (UMAP, PCA, HDBSCAN) correctly recover latent topics from the embedding space.
Background assumption of all BERTopic-style pipelines; not re-proved.

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Cite this review

Pith. "Pith review of Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities." pith.science (2026). https://pith.science/paper/LDEFDFUD

@misc{pith2026260703887,
  author       = {Pith},
  title        = {Pith review of: Advanced Topic Modeling Techniques for Categorizing Software Vulnerabilities},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LDEFDFUD}},
  note         = {Machine review of arXiv:2607.03887}
}

read the original abstract

The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively, highlighting the need for advanced solutions. This study leverages state-of-the-art topic modeling techniques powered by large language models (LLMs) to extract meaningful insights from the 'Threat' feature of a software vulnerability dataset. Models such as BERTopic, Top2Vec, CombinedTM, Llama2 with BERTopic, and Mixtral are utilized, along with dimensionality reduction and clustering methods like UMAP, PCA, HDBSCAN, and DBSCAN. By uncovering latent patterns and generating interpretable clusters, this research enhances threat prioritization and decision-making in cybersecurity. The findings support scalable and automated solutions for vulnerability management, contributing to improved security practices.

Figures

Figures reproduced from arXiv: 2607.03887 by the authors.

**Figure 1.** Proposed Workflow IV. RESULT ANALYSIS AND DISCUSSION A. Dataset Description and Visualization The dataset is collected from Cisco Labs and consists of 69,909 entries and 39 columns. This dataset provides an overview of software vulnerabilities, their vulnerability type and impact, and suggested remediation. It helps identify patterns and trends in vulnerabilities that could be used to enhance security measures and p… view at source ↗

**Figure 2.** Word Cloud of ‘Threat’ Descriptions [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 4.** Hierarchical Clustering: Visualizing topic relationships through a [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

Figures from the paper (5 more)

**Figure 3.** Figure 3: Similarity Matrix of BertTopic using UMAP and HDBSCAN: [PITH_FULL_IMAGE:figures/full_fig_p005_3.png]

**Figure 6.** Figure 6: Hierarchical Clustering Dendrogram: Visualizing topic relationships [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]

**Figure 5.** Figure 5: Topic Word Score Bar Chart: Visualizing significant words for each [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 8.** Figure 8: Heatmap: Visualizing thematic overlaps and distinctions between [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]

**Figure 9.** Figure 9: Heatmap: Visualizing thematic overlaps and distinctions between [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]

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Reference graph

Works this paper leans on

20 extracted references · 1 canonical work pages

[1]

”GPTopic: Dynamic and Interactive Topic Represen- tations.” arXiv preprint arXiv:2403.03628 (2024)

Reuter, Arik, et al. ”GPTopic: Dynamic and Interactive Topic Represen- tations.” arXiv preprint arXiv:2403.03628 (2024)

arXiv 2024
[2]

”Towards interpreting topic models with ChatGPT.” The 20th World Congress of the International Fuzzy Systems Associa- tion

Rijcken, Emil, et al. ”Towards interpreting topic models with ChatGPT.” The 20th World Congress of the International Fuzzy Systems Associa- tion. 2023

2023
[3]

Matos-Carvalho, and Nuno Fachada

Petukhova, Alina, Joao P. Matos-Carvalho, and Nuno Fachada. ”Text clustering with LLM embeddings.” arXiv preprint arXiv:2403.15112 (2024)

arXiv 2024
[4]

”Large-scale vulnerability analysis.” Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense

Frei, Stefan, et al. ”Large-scale vulnerability analysis.” Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense. 2006

2006
[5]

Yu, Dhanya Aravind, and Passarawarin Supthaweesuk

Weider, D. Yu, Dhanya Aravind, and Passarawarin Supthaweesuk. ”Soft- ware Vulnerability Analysis for Web Services Software Systems.” iscc. 2006

2006
[6]

”Enhancing Short- Text Topic Modeling with LLM-Driven Context Expansion and Prefix- Tuned V AEs.” arXiv preprint arXiv:2410.03071 (2024)

Akash, Pritom Saha, and Kevin Chen-Chuan Chang. ”Enhancing Short- Text Topic Modeling with LLM-Driven Context Expansion and Prefix- Tuned V AEs.” arXiv preprint arXiv:2410.03071 (2024)

arXiv 2024
[7]

”Topic Modeling with Fine-tuning LLMs and Bag of Sentences.” arXiv preprint arXiv:2408.03099 (2024)

Schneider, Johannes. ”Topic Modeling with Fine-tuning LLMs and Bag of Sentences.” arXiv preprint arXiv:2408.03099 (2024)

arXiv 2024
[8]

”Large Language Models Offer an Alternative to the Tra- ditional Approach of Topic Modelling.” arXiv preprint arXiv:2403.16248 (2024)

Mu, Yida, et al. ”Large Language Models Offer an Alternative to the Tra- ditional Approach of Topic Modelling.” arXiv preprint arXiv:2403.16248 (2024)

arXiv 2024

Show all 20 references

[9]

”Leveraging LLMs for Efficient Topic Reviews.” Applied Sciences 14.17 (2024): 7675

Gana, Bady, et al. ”Leveraging LLMs for Efficient Topic Reviews.” Applied Sciences 14.17 (2024): 7675

2024
[10]

”LLM Reading Tea Leaves: Automatically Eval- uating Topic Models with Large Language Models.” arXiv preprint arXiv:2406.09008 (2024)

Yang, Xiaohao, et al. ”LLM Reading Tea Leaves: Automatically Eval- uating Topic Models with Large Language Models.” arXiv preprint arXiv:2406.09008 (2024)

2024 arXiv
[11]

”Generating topic-agnostic conversations With LLMs.” IEEE access (2024)

Sandilya, Harshit, et al. ”Generating topic-agnostic conversations With LLMs.” IEEE access (2024)

2024
[12]

”Software vulnerability analysis and discovery using deep learning techniques: A survey.” IEEE Access 8 (2020): 197158- 197172

Zeng, Peng, et al. ”Software vulnerability analysis and discovery using deep learning techniques: A survey.” IEEE Access 8 (2020): 197158- 197172

2020
[13]

”A vulnerability analysis and prediction framework.” Computers & Security 92 (2020): 101751

Williams, Mark A., et al. ”A vulnerability analysis and prediction framework.” Computers & Security 92 (2020): 101751

2020
[14]

Real Time Analysis of Air Pollution Pre- diction using IoT

Niranjan D K, N Rakesh, “Real Time Analysis of Air Pollution Pre- diction using IoT”, in the 2nd International Conference on Inventive Research in Computing Application [ICIRCA 2020], July-2020

2020
[15]

Smart Surveillance System by Face Recog- nition and Tracking using Machine Learning Techniques

Niranjan D K, N Rakesh, “Smart Surveillance System by Face Recog- nition and Tracking using Machine Learning Techniques”, in the 4th International Conference on Computational Vision and Bio Inspired Computing [ICCVBIC 2020], November-2020

2020
[16]

A Real- Time Human Computer Interaction Using Hand Gestures in OpenCV

Sainadh. K.V ., Satwik. K., Ashrith. V ., Niranjan. D.K. (2023), “A Real- Time Human Computer Interaction Using Hand Gestures in OpenCV”, in the Information and Communication Technology for Intelligent Sys- tems. ICTIS 2023. Lecture Notes in Networks and Systems, vol 720. Spri...

2023
[17]

Early Building Collapse Detection using IoT

N. .D.K. and Rakesh, N., “Early Building Collapse Detection using IoT”, in International Conference on Inventive Research in Computing Applications [ICIRCA 2020] organized by RVS College of Engineering and Technology, Coimbatore, India, 2020

2020
[18]

S. M, R. V . Savant, S. Seshadri, N. Narmada and P. B. Pati, ”Unveiling Hidden Patterns: Clustering Algorithms on C Code em- bedding,” 2024 IEEE 9th International Conference for Conver- gence in Technology (I2CT), Pune, India, 2024, pp. 1-7, doi: 10.1109/I2CT61223.2024.10543306

2024 doi
[19]

and Pati, P.B., 2024, June

Rao, S.S., Mishra, S., Akhilesh, S., Balakrishnan, R.M. and Pati, P.B., 2024, June. Automatic Assessment of Quadratic Equation Solutions Using MathBERT and RoBERTa Embeddings. In 2024 15th Inter- national Conference on Computing Communication and Networking Technologies (ICCCN...

2024
[20]

and Kumar, K.D., 2024

Anirudh, S., Nishant, P.R., Baitha, S. and Kumar, K.D., 2024. An Ensemble Classification Model for Phishing Mail Detection. Procedia Computer Science, 233, pp.970-978

2024

Pith tools

Reviewed July 11, 2026 · model on record in the stance chip above.

[1] [1]

”GPTopic: Dynamic and Interactive Topic Represen- tations.” arXiv preprint arXiv:2403.03628 (2024)

Reuter, Arik, et al. ”GPTopic: Dynamic and Interactive Topic Represen- tations.” arXiv preprint arXiv:2403.03628 (2024)

arXiv 2024

[2] [2]

”Towards interpreting topic models with ChatGPT.” The 20th World Congress of the International Fuzzy Systems Associa- tion

Rijcken, Emil, et al. ”Towards interpreting topic models with ChatGPT.” The 20th World Congress of the International Fuzzy Systems Associa- tion. 2023

2023

[3] [3]

Matos-Carvalho, and Nuno Fachada

Petukhova, Alina, Joao P. Matos-Carvalho, and Nuno Fachada. ”Text clustering with LLM embeddings.” arXiv preprint arXiv:2403.15112 (2024)

arXiv 2024

[4] [4]

”Large-scale vulnerability analysis.” Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense

Frei, Stefan, et al. ”Large-scale vulnerability analysis.” Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense. 2006

2006

[5] [5]

Yu, Dhanya Aravind, and Passarawarin Supthaweesuk

Weider, D. Yu, Dhanya Aravind, and Passarawarin Supthaweesuk. ”Soft- ware Vulnerability Analysis for Web Services Software Systems.” iscc. 2006

2006

[6] [6]

”Enhancing Short- Text Topic Modeling with LLM-Driven Context Expansion and Prefix- Tuned V AEs.” arXiv preprint arXiv:2410.03071 (2024)

Akash, Pritom Saha, and Kevin Chen-Chuan Chang. ”Enhancing Short- Text Topic Modeling with LLM-Driven Context Expansion and Prefix- Tuned V AEs.” arXiv preprint arXiv:2410.03071 (2024)

arXiv 2024

[7] [7]

”Topic Modeling with Fine-tuning LLMs and Bag of Sentences.” arXiv preprint arXiv:2408.03099 (2024)

Schneider, Johannes. ”Topic Modeling with Fine-tuning LLMs and Bag of Sentences.” arXiv preprint arXiv:2408.03099 (2024)

arXiv 2024

[8] [8]

”Large Language Models Offer an Alternative to the Tra- ditional Approach of Topic Modelling.” arXiv preprint arXiv:2403.16248 (2024)

Mu, Yida, et al. ”Large Language Models Offer an Alternative to the Tra- ditional Approach of Topic Modelling.” arXiv preprint arXiv:2403.16248 (2024)

arXiv 2024